A total of 72 individuals of genus Pristina were sorted from aquatic plant, Ceratophyllum demersum L., and filamentous algae collected from three sites on Tigris River at Baghdad including: Al-Sarafiya area (S1), Al- Jadiriyah area (S2), and Al- Za´afaraniya area (S3). Four species were identified including P. longiseta, P. aequiseta, P. proboscidea and P. foreli, with percentags of 51.7 , 36.4, 1.1, and 10.5 % respectively. The first two species found in all sites , while , P. proboscidea found only in S1 and P. foreli only in S2.

ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, auto

The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in perme

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

The study was conducted in the Tigris River in Baghdad during May 2021 until March 2022 to follow the impact of climate change, rising temperatures, and the presence of pollutants on the dynamics of phytoplankton and some physicochemical variables from four sites. The results showed that the climatic conditions during different seasons, in addition to the nature of the sampling sites, have a clear and significant impact on the studied traits and, in turn, affect the phytoplankton community. The highest average temperature (30.67 ˚C) was recorded; the pH values ranged between 8.70 & 6.75; the electrical conductivity (1208.18-770.11 µS/cm ) and the total dissolved solids (TDS) (778.95- 439.49 mg/L) were evaluated. Upon measuring

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Sewage water is a mixture of water and solids added to water for various uses, so it needs to be treated to meet local or global standards for environmentally friendly waste production. The present study aimed to analyze the new Maaymyrh sewage treatment plant's quality parameters statistically at Hilla city. The plant is designed to serve 500,000 populations, and it is operating on a biological treatment method (Activated Sludge Process) with an average wastewater inflow of 107,000m³/day. Wastewater data were collected daily by the Mayoralty of Hilla from November 2019 to June 2020 from the influent and effluent in the (STP) new in Maaymyrh for five water quality standards, such as (BOD5), (COD), (TSS), (TP)